Import Geant4 11.2.0 source tree

examples/advanced/exp_microdosimetry/1_plot_distributions.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import glob
+
+def Normalize(f, x) :
+	dx = x[1:] - x[:-1]
+	integral = np.sum(f[:-1] * dx)
+	f_norm = f / integral
+	return f_norm
+
+# When running in MT and outputting to cvs, each thread outputs to a different file.
+# Therefore I need to cycle through each thread and append them all together.
+
+energy = np.empty([0,1])
+length = np.empty([0,1])
+
+ntuple_name = "radioprotection_nt_102"
+output_name = ntuple_name + "_t" + "*" + ".csv"
+output_list = glob.glob(output_name)
+
+for this_file in output_list :	
+	energy_thread, length_thread = np.loadtxt(this_file, delimiter=',', unpack=True, usecols=(0,1))
+	
+	energy = np.append(energy, energy_thread)
+	length = np.append(length, length_thread)
+
+# Experimentally, the mean path length is calculated geometrically as a mean chord length.
+# Here for convenience it's taken by averaging the effective path lengths.
+mean_path_length = np.average(length)
+
+# A conversion factor can be used to convert the target material to water or tissue equivalent.
+# Its choice depends on the material and can be calculated in different ways.
+# It is suggested that the user replace the following value with his own.
+conversion_factor = 1.
+
+# If the previous value is not overriden by the user, this script will attempt to read geometry.mac
+# and provide a factor accordingly
+if conversion_factor == 1. :
+	detector = np.array(["Diamond", "MicroDiamond", "Silicon", "SiliconBridge"])
+	factor = np.array([0.32, 0.32, 0.57, 0.57])	# conversion factor based on material stopping power
+	
+	with open("geometry.mac") as search:
+		for line in search:
+			line = line.rstrip()  # remove new line
+			
+			for this in detector :
+				match = "/geometrySetup/selectDetector " + this
+				
+				if match == line:
+					conversion_factor = factor[ detector == this ][0]
+				else:
+					conversion_factor = factor[ detector == "Diamond" ][0]	# default detector type (no macro used)
+
+
+y = energy * conversion_factor / mean_path_length
+
+
+# The spectrum is now binned logarithmically, to avoid oscillations at higher energies
+# (due to fewer counts) that wouldn't much meaning.
+
+minimum = np.amin(y)
+maximum = np.amax(y)
+
+exp_start = np.floor(np.log10(minimum))
+exp_end = np.ceil(np.log10(maximum))
+
+n_decades = int(exp_end - exp_start)
+
+# Number of logarithmic bins per decade:
+# Higher values give better resolution, but lead to oscillations
+# (especially at high energy) if your statistic has too few counts.
+bins_per_dec = 60
+
+n_bins = n_decades * bins_per_dec
+
+y_bins = np.zeros(n_bins)
+
+y_bins[0] = 10**exp_start
+
+for i in range(1, n_bins) :
+	y_bins[i] = y_bins[i-1] * 10**( 1 / bins_per_dec )
+
+
+# Create the histogram
+
+# For now f is a number of counts...
+f = np.histogram( y, bins=y_bins ) [0]
+
+tot_counts = np.sum(f)
+
+# ... so now I turn f into a density
+bin_width = y_bins[1:] - y_bins[:-1]
+f = f / bin_width
+
+f = np.append(f, 0.)	# give f and y_bins arrays the same size
+
+# Normalize the spectra to unit area under the curve
+f = Normalize(f, y_bins)
+d = Normalize(y_bins*f, y_bins)
+
+
+# Save to file
+
+output_file = "analysed_spectra.csv"
+header = "y[keV/um], f(y)[um/keV], d(y)[um/keV]"
+
+np.savetxt( output_file, np.c_[ y_bins, f, d ], header=header, delimiter=',' )
+
+
+# Plot
+
+fig, ax1 = plt.subplots()
+
+color = 'tab:blue'
+
+ax1.semilogx( y_bins, y_bins * f, linewidth=0.5, color=color  )
+
+ax1.set_xlabel(r'$y \,\, [keV / \mu m]$')
+ax1.set_ylabel(r'$y \cdot f(y) $', color=color)
+ax1.tick_params(axis='y', labelcolor=color)
+
+ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
+
+color = 'tab:red'
+
+ax2.semilogx( y_bins, y_bins * d, linewidth=0.5, color=color )
+
+ax2.set_ylabel(r'$y \cdot d(y) $', color=color)
+ax2.tick_params(axis='y', labelcolor=color)
+
+title = str(tot_counts) + " counts, " + str(bins_per_dec) + " bins per decade, " + str(conversion_factor) + " conversion factor"
+fig.suptitle(title)
+
+fig.tight_layout()
+
+plt.subplots_adjust(top=0.92)
+plt.show()